python regression vowpalwabbit with FEARTURES INTERACTION

vowpalwabbit python ridge regression with FEARTURES INTERACTION ridge regression FEATURES BOTH CATEGORICAL AND CONTINUES IMPORTANT to HAVE INTERACTION BETWEEN CATEGORICAL FEARTURES : SECOND ORDER AND THIRED ORDER create namespace one for categorical and one for continues features code interactoins between all categorical values like from vowpalwabbit.sklearn_vw import VW, VWClassifier, VWRegressor vw_squared = VWRegressor(loss_function='squared' , normalized = True, interactions = 'aaa') but better to use from vowpalwabbit import pyvw data [login to view URL] make sure you code performance is better than baselines train A) predict with good performance by using train data file to train and predict ( no split to train and test data sets) B) predict with good performance by using test data for prediction and train data set to train model : do split to train and test data sets PROVE YOUR CODE IS CORRECT 1 correct performance dynamics for parameters changing for example what will be if this parameter to change: better or worse performance? 2 good performance - better performance than base line ( use good base line ) 3 code logic to help you some links below code starter [login to view URL] [login to view URL] [login to view URL] [login to view URL] [login to view URL] [login to view URL]:~:text=Use%20chunksize%20to%20read%20a,be%20read%20in%20per%20chunk. [login to view URL] [login to view URL] [login to view URL] vw [login to view URL] -f [login to view URL] –binary –passes 20 -c -q ff –sgd –l1 0.00000001 –l2 0.0000001 –learning_rate 0.5 –loss_function logistic [login to view URL] test3 <- c("-t", [login to view URL]("test", "train-sets", "[login to view URL]", package="RVowpalWabbit"), "-f", [login to view URL](tempdir(), "[login to view URL]"), "--cache_file", [login to view URL](tempdir(), "[login to view URL]")) also [login to view URL] many example for VW maybe >>> from [login to view URL] import DFtoVW >>> import pandas as pd >>> df = [login to view URL]({"y": [1], "x": [2]}) >>> conv = DFtoVW.from_colnames(y="y", x="x", df=df) >>> conv.convert_df() ['1 | x:2']